Course in Ethics in Big Data and National Security
Online 300-hour course in ethics in big data and national security. 6 modules covering Directive (EU) 2016/680 (data in police and criminal matters), Organic Law 7/2021 (data protection for criminal purposes) and Apache Spark. Assigned tutor and enrolment open.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Analyse the use of mass data in national security and its risks to fundamental rights.
Interpret the European and Spanish legal framework on data processing for security purposes.
Apply the principles of necessity and proportionality to data analysis programmes.
Design technical safeguards such as pseudonymisation, access control and traceability.
Prepare data protection impact assessments for mass analysis systems.
Propose accountability mechanisms and independent oversight.
Argue well-founded positions in the public debate on surveillance and security.
Who it's for
- Data protection officer in the public sector who wishes to specialise in Ethics in Big Data and National Security.
- Data ethics analyst who wishes to specialise in Ethics in Big Data and National Security.
- Compliance adviser in intelligence who wishes to specialise in Ethics in Big Data and National Security.
- Information systems auditor who wishes to specialise in Ethics in Big Data and National Security.
- Researcher in surveillance and fundamental rights who wishes to specialise in Ethics in Big Data and National Security.
- Data governance consultant who wishes to specialise in Ethics in Big Data and National Security.
- Engineering or science graduates seeking to enter public administration.
What you'll work with
- Directive (EU) 2016/680 (data in police and criminal matters)
- Organic Law 7/2021 (data protection for criminal purposes)
- European Convention on Human Rights, Art. 8 (private life)
- Council of Europe Convention 108+
- Organic Law 2/2002 (prior judicial control of the CNI)
- Directive (EU) 2016/681 (Passenger Name Record, PNR)
- Regulation (EU) 2024/1689 (AI Act)
6 modules · 300 h
1.1What changes when data is massive1.2Sources: communications, mobility, finance and open sources1.3Data analytics applied to security1.4Real promises and limits of prediction1.5Targeted surveillance versus mass surveillance1.6Deterrent effect on civil rights1.7Public cases that shaped the debate1.8Actors: states, companies and data intermediaries1.9Technical and legal vocabulary1.10Exercise: mapping the data of a programme
2.1Right to private life and data protection2.2National security exceptions and their conditions2.3Principles of necessity and proportionality2.4Judicial authorisation and prior control2.5European case law on data retention2.6International data transfers2.7Access by authorities to company data2.8Intelligence regulation and parliamentary oversight2.9Safeguards for the affected person2.10Practice: analysing the legality of an access
3.1Absence of consent and alternative legitimacy3.2Bias in data and in models3.3False positives and the human cost of error3.4Profiling and indirect discrimination3.5Algorithmic opacity and the right to explanation3.6Reuse of data for different purposes3.7Aggregation and re-identification3.8Data on minors and vulnerable groups3.9Analyst responsibility3.10Debate: a controversial profiling case
4.1Minimisation and purpose limitation in practice4.2Pseudonymisation, encryption and segregated access4.3Access logging and auditing4.4Data protection impact assessment4.5Fundamental rights impact assessment4.6Independent oversight and supervisory authorities4.7Robustness and bias testing of models4.8Documentation of the model and the data4.9Retention, deletion and data lifecycle4.10Practice: impact assessment of a system
5.1Possible transparency on security programmes5.2Public reports and aggregate statistics5.3Parliamentary oversight and specialised committees5.4Whistleblower protection and known cases5.5The role of journalism and civil society5.6Strategic litigation and its effect5.7International cooperation and asymmetries5.8Public trust as a security asset5.9Foreseeable regulatory agenda5.10Practice: designing a public transparency report
6.1Choice of a specific programme or system6.2Description of the data and the analysis6.3Identification of those affected and of the risks6.4Legal analysis of legitimacy6.5Ethical analysis of the dilemmas6.6Design of technical and organisational safeguards6.7Oversight and auditing mechanisms6.8Transparency plan6.9Analysis report6.10Defence of the work
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the enrolment period, which makes it possible to combine it with work.
Each module combines theory, guided reading of the original regulation and applied exercises. The working software is Apache Spark, Python (pandas), ARX Data Anonymization Tool, Neo4j, in free, academic or evaluation versions. Case studies are based on real procedures for data protection impact assessments, analysis of mass surveillance cases and simulations of oversight committees. Assessment is continuous per module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Analyse the use of mass data in national security and its risks to fundamental rights.
Interpret the European and Spanish legal framework on data processing for security purposes.
Apply the principles of necessity and proportionality to data analysis programmes.
Design technical safeguards such as pseudonymisation, access control and traceability.
Prepare data protection impact assessments for mass analysis systems.
Where this takes you
Requirements
- No prior degree required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme is 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You work with Apache Spark, Python (pandas), ARX Data Anonymization Tool, Neo4j. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or Postgraduate Diploma in the same area count towards the higher programme. Check the credit transfer policy when you enrol.
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